The Affects on the Growth of Trees

Introduction

Trees are important to our environment as they provide home and food to many different organisms; they also take up carbon dioxiide and release oxygen into our ecosystem. Many different factors can determine trees' growth, but sunlight, water and nutrients are essential for their growth.

This project analyzes trees planted on streets of Metro Vancouver and examines their growth based on different category variables provided.

Question(s) of interests

1. Do different neighbourhoods have differet affects on trees' growth?
2. Does the number of trees planted in certain areas have an affect on trees' growth?
3. Does the presence of root barrier have an affect on trees' growth?
4. Do different genera of trees have different growth rates?

import pandas as pd
import altair as alt

The Tree Data

trees = pd.read_csv('small_unique_vancouver.csv', parse_dates = ['date_planted'])
trees.head()

	Unnamed: 0	std_street	on_street	species_name	neighbourhood_name	date_planted	diameter	street_side_name	genus_name	assigned	...	plant_area	curb	tree_id	common_name	height_range_id	on_street_block	cultivar_name	root_barrier	latitude	longitude
0	10747	W 20TH AV	W 20TH AV	PLATANOIDES	Riley Park	2000-02-23	28.5	EVEN	ACER	N	...	15	Y	21421	NORWAY MAPLE	4	0	NaN	N	49.252711	-123.106323
1	12573	W 18TH AV	W 18TH AV	CALLERYANA	Arbutus-Ridge	1992-02-04	6.0	ODD	PYRUS	N	...	7	Y	129645	CHANTICLEER PEAR	2	2300	CHANTICLEER	N	49.256350	-123.158709
2	29676	ROSS ST	ROSS ST	NIGRA	Sunset	NaT	12.0	ODD	PINUS	N	...	7	Y	154675	AUSTRIAN PINE	4	7800	NaN	N	49.213486	-123.083254
3	8856	DOMAN ST	DOMAN ST	AMERICANA	Killarney	1999-11-12	11.0	EVEN	FRAXINUS	N	...	7	Y	180803	AUTUMN APPLAUSE ASH	4	6900	AUTUMN APPLAUSE	N	49.220839	-123.036721
4	21098	EAST BOULEVARD	EAST BOULEVARD	HIPPOCASTANUM	Shaughnessy	NaT	15.5	ODD	AESCULUS	Y	...	N	Y	74364	COMMON HORSECHESTNUT	4	5200	NaN	N	49.238514	-123.154958

5 rows × 21 columns

The first column does not appear to be useful as it is an identifier, therefore it will be dropped.

trees.drop(columns = trees.columns[0], inplace=True)
trees.info()

<class 'pandas.core.frame.DataFrame'>
RangeIndex: 5000 entries, 0 to 4999
Data columns (total 20 columns):
 #   Column              Non-Null Count  Dtype         
---  ------              --------------  -----         
 0   std_street          5000 non-null   object        
 1   on_street           5000 non-null   object        
 2   species_name        5000 non-null   object        
 3   neighbourhood_name  5000 non-null   object        
 4   date_planted        2363 non-null   datetime64[ns]
 5   diameter            5000 non-null   float64       
 6   street_side_name    5000 non-null   object        
 7   genus_name          5000 non-null   object        
 8   assigned            5000 non-null   object        
 9   civic_number        5000 non-null   int64         
 10  plant_area          4950 non-null   object        
 11  curb                5000 non-null   object        
 12  tree_id             5000 non-null   int64         
 13  common_name         5000 non-null   object        
 14  height_range_id     5000 non-null   int64         
 15  on_street_block     5000 non-null   int64         
 16  cultivar_name       2658 non-null   object        
 17  root_barrier        5000 non-null   object        
 18  latitude            5000 non-null   float64       
 19  longitude           5000 non-null   float64       
dtypes: datetime64[ns](1), float64(3), int64(4), object(12)
memory usage: 781.4+ KB

The tree dataset has 5000 observations and 21 columns; all columns except date_planted, plant_area, and cultivar_name have full observation. date_planted has more than half of the observation missing, plant_name has 50 missing, and cultivar_name has just under half of the observation missing. The data has 12 string objects, 3 float values, 3 integer values, and 1 datetime value.

trees.describe(include = 'all', datetime_is_numeric=True)

	std_street	on_street	species_name	neighbourhood_name	date_planted	diameter	street_side_name	genus_name	assigned	civic_number	plant_area	curb	tree_id	common_name	height_range_id	on_street_block	cultivar_name	root_barrier	latitude	longitude
count	5000	5000	5000	5000	2363	5000.000000	5000	5000	5000	5000.000000	4950	5000	5000.000000	5000	5000.00000	5000.000000	2658	5000	5000.000000	5000.000000
unique	603	607	171	22	NaN	NaN	4	67	2	NaN	38	2	NaN	361	NaN	NaN	176	2	NaN	NaN
top	W 13TH AV	CAMBIE ST	SERRULATA	Renfrew-Collingwood	NaN	NaN	ODD	ACER	N	NaN	10	Y	NaN	KWANZAN FLOWERING CHERRY	NaN	NaN	KWANZAN	N	NaN	NaN
freq	52	49	463	384	NaN	NaN	2554	1218	4564	NaN	736	4593	NaN	383	NaN	NaN	383	4679	NaN	NaN
mean	NaN	NaN	NaN	NaN	2003-09-06 04:03:08.912399488	12.340888	NaN	NaN	NaN	2975.707600	NaN	NaN	128682.584600	NaN	2.73440	2960.227000	NaN	NaN	49.247349	-123.107128
min	NaN	NaN	NaN	NaN	1989-10-31 00:00:00	0.000000	NaN	NaN	NaN	2.000000	NaN	NaN	36.000000	NaN	0.00000	0.000000	NaN	NaN	49.202783	-123.220560
25%	NaN	NaN	NaN	NaN	1997-11-06 00:00:00	4.000000	NaN	NaN	NaN	1300.500000	NaN	NaN	61321.500000	NaN	2.00000	1300.000000	NaN	NaN	49.230152	-123.144178
50%	NaN	NaN	NaN	NaN	2003-02-12 00:00:00	10.000000	NaN	NaN	NaN	2639.000000	NaN	NaN	130130.500000	NaN	2.00000	2600.000000	NaN	NaN	49.247981	-123.105861
75%	NaN	NaN	NaN	NaN	2009-11-17 00:00:00	18.000000	NaN	NaN	NaN	4123.000000	NaN	NaN	191332.000000	NaN	4.00000	4100.000000	NaN	NaN	49.263275	-123.063484
max	NaN	NaN	NaN	NaN	2019-05-07 00:00:00	71.000000	NaN	NaN	NaN	9113.000000	NaN	NaN	270750.000000	NaN	9.00000	9100.000000	NaN	NaN	49.293930	-123.023311
std	NaN	NaN	NaN	NaN	NaN	9.266600	NaN	NaN	NaN	2078.580429	NaN	NaN	75412.260406	NaN	1.56957	2086.861052	NaN	NaN	0.021251	0.049137

Based on the questions of interest, only the columns neighbourhood_name, date_planted, diameter, genus_name, height_range_id, and root_barrier will be used. The other columns will be dropped. The column date_planted is formatted using YYYY-MM-DD. As only the year will be useful, it will be extracted and converted to integer for easier access.

trees['year_planted'] = trees['date_planted'].dt.year.astype('Int64')
trees = trees[['neighbourhood_name', 'year_planted', 'diameter', 'genus_name', 'height_range_id', 'root_barrier']]
trees.head()

	neighbourhood_name	year_planted	diameter	genus_name	height_range_id	root_barrier
0	Riley Park	2000	28.5	ACER	4	N
1	Arbutus-Ridge	1992	6.0	PYRUS	2	N
2	Sunset	<NA>	12.0	PINUS	4	N
3	Killarney	1999	11.0	FRAXINUS	4	N
4	Shaughnessy	<NA>	15.5	AESCULUS	4	N

Exploratory Visualizations

Question 1: Do different neighbourhoods have differet affects on trees' growth?

Before diving into the question, let's visualize how many trees are present within each neighbourhood. It could be a good indication that if certain neighbourhoods have more trees, they would have more resources for trees to grow on.

alt.Chart(trees).mark_bar().encode(
    alt.X('neighbourhood_name', sort = '-y', title = "Neighbourhood"),
    alt.Y('count()')
).properties(title = "Number of trees present in each neighbourhood")

According to the bar graph of the number of trees in each neighbourhood, the neighbourhoods with the most trees planted are Renfrew-Collingwood and Kensington-Cedar Cottage; they each have over 350 trees. The neighbourhood with the least number of trees is Strathcona with less than 100.

Trees grow primarily in their height and length while their thickness is determined by secondary growth. As the growth in their height is the primary growth, I will be only focusing on the column height_range_id.

Let's take a look at the height of the trees in each neighbourhood for each year planted.

alt.Chart(trees, width = 190, height = 80).mark_bar().encode(
    alt.X('neighbourhood_name', title = "Neighbourhood", sort = '-y'),
    alt.Y('mean(height_range_id)', title = "Mean of height")
).facet('year_planted', columns = 4, title = "Mean diameter of trees in each neighbourhood for different years").resolve_scale(y = 'independent', x = 'independent')

From the faceted graph, trees' growth and their neighbourhood do not appear to be correlated with one another.

Question 2: Does the number of trees planted in certain areas have an affect on the trees' growth?

If more trees are planted in certain areas than others, they would need to share and compete for resources, which would slow down their growth. Let's compare the graphs of the average height of trees for each year and the total number of trees planted each year.

height_mean_chart = alt.Chart(trees).mark_circle().encode(
    alt.X('year_planted', title = "Year", scale = alt.Scale(domain = [1988, 2020])),
    alt.Y('mean(height_range_id)', title = "Height range mean")
).properties(title = "Average trees' height range for each year planted")
height_mean_chart

tree_yearly_chart = alt.Chart(trees).mark_bar().encode(
    alt.Y('count()'),
    alt.X('year_planted', title = "Year", scale = alt.Scale(domain = [1988, 2020]))
).properties(title = "Total number of trees planted")
tree_yearly_chart

Some correlation appears to be presence between the average height of trees' growth and the total number of trees planted each year. In the years where less trees are planted compared to a few years prior or after (1991, 2003), the average height of the trees appears to be taller.

Question 3: Does the presence of root barrier have any affects on tree's growth?

Let's explore how many trees within the dataset have root barriers installed.

barrier_chart = alt.Chart(trees).mark_bar().encode(
    alt.Y('root_barrier', title = "Root barrier"),
    alt.X('count()')
).properties(title = "Number of trees with and without root barriers")
barrier_chart

The bar graph shows that less than 10% of the tree dataset do not have root barrier installed. Let's now compare the mean height of trees for the presence and absence of root barrier.

barrier_height_chart = alt.Chart(trees).mark_bar().encode(
    alt.X('mean(height_range_id)', title = "Average tree height range"),
    alt.Y('root_barrier', title = "Root barrier")
).properties(title = "Average trees' height for root barrier")
barrier_height_chart

Trees without root barriers appear to have higher average height compared to trees with root barriers. They grow twice as much as those with root barriers.

Question 4: Do different genera of trees have different growth rates?

alt.Chart(trees, width = 80, height = 450).mark_bar().encode(
    alt.Y('genus_name', sort = '-x', title = "Genus"),
    alt.X('mean(height_range_id)', title = "Average height")
).facet('year_planted', columns = 4, title = "Average height of each genera of tree every year").resolve_scale(y = 'independent', x = 'independent')

From the above graph, some genera of trees appear to grow taller others. The most obvious appears to be of the genus Platanus as for the years that it appears in, it is located within the top rows.

Conclusion

The categorical variables of interest neighbourhood_name and genus_name have many different unique values and can often get confusing. It would be of best interest to filter the data so that the columns would have only around 10 unique values. Similarly, with the column year_planted, it would be of best interest to filter the data so that the data would only contain trees from 1992 to 2014 since most trees were planted within those years.

The 4 graphs which will be useful in the final project are the first, second, sixth, and seventh graphs as they seem most fitting with the questions of interest.

Resources

1. Data Vizualization
2. Street tree data
3. Trees